In order to achieve reliable level of performance for a large volume of computing transaction, the workload profiles need to be evaluated and a suitable architecture needs to be defined. High performance computing solutions can be designed to address and respond to the defined workloads in several ways, such as Parallel, Cluster or Grid. It is important to consider not only the aggregate of individual transaction processing loads, but also the I/O and data access models and the cycles of ebb and flow of transaction concurrency.

Parallel Computing

Parallel computing allows arrays of compute instances to process simultaneously to execute a single large calculation or related transactions. Parallel computing can be used to address complex problems by taking large interrelated workloads and breaking them out across the array into separate computational tasks that are carried out at the same time in concert. These systems be widely distributed and can scale to proportions only limited by the number of nodes working in parallel, but also requires system level coordination to manage the parallelism, to break out the processes and then reassemble the outcomes.

Cluster Computing

High performance computing clusters use multiple nodes that are linked together through locan network technologies to create a cluster of compute capability. This acts as a single computer, yet one with cutting edge computational power. The configuration of an HPC cluster is uniquely designed to solve one problem by spanning it across the nodes in a system. HPC clusters have a defined network topology and allow organizations to tackle advanced computations with uncompromised processing speeds.

Grid, or Distributed Computing

Grid or distributed computing involve multiple computers connected through a network that share a common goal, such as solving a complex problem or performing a large computational task. This approach is ideal for addressing jobs that can be split into separate chunks and distributed across the grid. This is similar Each node within the system can perform tasks independently without having to communicate with other nodes.